This invention relates generally to motion capture technology, and more specifically to human body segmentation, tracking, and gesture recognition by use of digital video image data.
Motion capture technology refers generally to the science of tracking human movement in real time. Digitized human figure motion may be used to drive computer graphic characters. Processor based systems may detect and track human gestures or upper body features using digitized video image data. Applications for detecting and tracking human gestures or upper body features include human computer interfaces, human sign language understanding, industrial control, and entertainment devices such as virtual reality or interactive games. Digitized video image data also may provide input to a processor-based system to perform other functions or services.
In the past, motion capture technology could track a person who was outfitted with sensors. For example, data gloves or other external devices may be attached to the human body to track movements such as hand gestures in a variety of applications. The use of sensors attached to a person have a number of clear disadvantages including discomfort and delay to set up and attach the device to the person.
Increasingly, digital cameras and vision systems have been proposed and used for human gesture recognition. However, before digital video image data may be fully and practically used to detect and track human gestures and movement of the upper body, there are a number of other problems and difficulties that must be addressed. One problem is that a user-guided initialization has been required to identify one or more of a subject's features before any detecting and tracking commences, and this can be time and resource intensive. Another problem is that there exists inherent depth ambiguity when two dimensional images are used. For example, if the color of the subject's skin is used to detect and track gestures or movement of the face and/or hands, there still may be depth ambiguity resulting in errors in detecting and tracking. Other problems detecting and tracking gestures and upper body features are caused by variations in illumination, shadow effects, non-stationary backgrounds, partial occlusions and self-occlusions. For these reasons, there is a need for improved detection and tracking of human gestures and upper body movement using digital video image data.